download/cache with pooch, eliminate local data/ dependency

scripts/ex-gwf-advtidal.py

@@ -19,11 +19,11 @@
 import flopy
 import matplotlib.pyplot as plt
 import numpy as np
+import pooch
 from flopy.plot.styles import styles
 from flopy.utils.gridintersect import GridIntersect
 from modflow_devtools.misc import timed
 from shapely.geometry import Polygon
-import pooch
 
 # Simulation name and workspace
 
@@ -191,7 +191,7 @@ def build_model():
     ghb_spd = {0: ghb_spd}
     fname = pooch.retrieve(
         url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/tides.csv",
-        known_hash="md5:425337a0bf24fa72c9e40f4e3d9f698a"
+        known_hash="md5:425337a0bf24fa72c9e40f4e3d9f698a",
     )
     tsdict = get_timeseries(fname, "tides", "linear")
     ghbobs_dict = {}
@@ -230,7 +230,7 @@ def build_model():
     ]
     fname = pooch.retrieve(
         url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/wellrates.csv",
-        known_hash="md5:6ca7366be279d679b14e8338a195422f"
+        known_hash="md5:6ca7366be279d679b14e8338a195422f",
     )
     tsdict = get_timeseries(
         fname,
@@ -263,7 +263,7 @@ def build_model():
     riv_spd = list(zip(rivlay, rivrow, rivcol, rivstg, rivcnd, rivrbt, rivbnd))
     fname = pooch.retrieve(
         url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/riverstage.csv",
-        known_hash="md5:83f8b526ec6e6978b1d9dbd6fde231ef"
+        known_hash="md5:83f8b526ec6e6978b1d9dbd6fde231ef",
     )
     tsdict = get_timeseries(
         fname,
@@ -281,7 +281,7 @@ def build_model():
     hashes = [
         "f8b9b26a3403101f3568cd42f759554f",
         "c1ea7ded8edf33d6d70a1daf2524584a",
-        "9ca294d3260c9d3c3487f8db498a0aa6"
+        "9ca294d3260c9d3c3487f8db498a0aa6",
     ]
     for ipak, p in enumerate([recharge_zone_1, recharge_zone_2, recharge_zone_3]):
         ix = GridIntersect(gwf.modelgrid, method="vertex", rtree=True)
@@ -298,7 +298,7 @@ def build_model():
             )
         fname = pooch.retrieve(
             url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/recharge{ipak + 1}.csv",
-            known_hash=f"md5:{hashes[ipak]}"
+            known_hash=f"md5:{hashes[ipak]}",
         )
         tsdict = get_timeseries(
             fname,

scripts/ex-gwf-bcf2ss.py

@@ -17,14 +17,14 @@
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
+import pooch
 from flopy.plot.styles import styles
 from modflow_devtools.misc import timed
 
 # Simulation name and workspace
 
 sim_name = "ex-gwf-bcf2ss"
 ws = pl.Path("../examples")
-data_ws = pl.Path("../data")
 
 # Configuration
 
@@ -43,7 +43,10 @@
 
 # Load the wetdry array for layer 1
 
-pth = data_ws / sim_name / "wetdry01.txt"
+pth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/wetdry01.txt",
+    known_hash="md5:3a4b357b7d2cd5175a205f3347ab973d",
+)
 wetdry_layer0 = np.loadtxt(
     pth,
 )

scripts/ex-gwf-capture.py

@@ -23,6 +23,7 @@
 import matplotlib.pyplot as plt
 import modflowapi
 import numpy as np
+import pooch
 from flopy.plot.styles import styles
 from modflow_devtools.misc import timed
 
@@ -48,16 +49,21 @@
 
 # Load the bottom, hydraulic conductivity, and idomain arrays
 
-bottom = np.loadtxt(
-    os.path.join("..", "data", sim_name, "bottom.txt"),
+pth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/bottom.txt",
+    known_hash="md5:201758a5b7febb0390b8b52e634be27f",
 )
-k11 = np.loadtxt(
-    os.path.join("..", "data", sim_name, "hydraulic_conductivity.txt"),
+bottom = np.loadtxt(pth)
+pth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/hydraulic_conductivity.txt",
+    known_hash="md5:6c78564ba92e850d7d51d6e957b8a3ff",
 )
-idomain = np.loadtxt(
-    os.path.join("..", "data", sim_name, "idomain.txt"),
-    dtype=np.int32,
+k11 = np.loadtxt(pth)
+pth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/idomain.txt",
+    known_hash="md5:435d4490adff7a35d1d4928661e45d81",
 )
+idomain = np.loadtxt(pth, dtype=np.int32)
 
 
 # Table Capture Fraction Model Parameters

scripts/ex-gwf-csub-p01.py

@@ -19,14 +19,14 @@
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
+import pooch
 from flopy.plot.styles import styles
 from modflow_devtools.misc import timed
 
 # Simulation name and workspace
 
 sim_name = "ex-gwf-csub-p01"
 ws = pl.Path("../examples")
-data_ws = pl.Path("../data")
 
 # Configuration
 
@@ -82,7 +82,10 @@
 
 # Load the aquifer load time series
 
-pth = data_ws / sim_name / "train_load_193704231304.csv"
+pth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/train_load_193704231304.csv",
+    known_hash="md5:32dc8e7b7e39876374af43605e264725",
+)
 csv_load = np.genfromtxt(pth, names=True, delimiter=",")
 
 # Reformat csv data into format for MODFLOW 6 timeseries file

scripts/ex-gwf-csub-p02.py

@@ -35,7 +35,6 @@
 
 sim_name = "ex-gwf-csub-p02"
 ws = pl.Path("../examples")
-data_ws = pl.Path("../data")
 
 # Scenario parameters
 

scripts/ex-gwf-csub-p03.py

@@ -34,7 +34,6 @@
 
 sim_name = "ex-gwf-csub-p03"
 ws = pl.Path("../examples")
-data_ws = pl.Path("../data")
 
 # Configuration
 

scripts/ex-gwf-csub-p04.py

@@ -25,7 +25,6 @@
 
 sim_name = "ex-gwf-csub-p03"
 ws = pl.Path("../examples")
-data_ws = pl.Path("../data")
 
 # Configuration
 

scripts/ex-gwf-disvmesh.py

@@ -19,6 +19,7 @@
 import flopy.utils.cvfdutil
 import matplotlib.pyplot as plt
 import numpy as np
+import pooch
 from flopy.plot.styles import styles
 from flopy.utils.geometry import get_polygon_area
 from flopy.utils.gridintersect import GridIntersect
@@ -32,7 +33,6 @@
 # Simulation name and workspace
 sim_name = "ex-gwf-disvmesh"
 ws = pl.Path("../examples")
-data_ws = pl.Path("../data")
 
 # Configuration
 
@@ -110,7 +110,10 @@ def from_argus_export(fname):
 
 # Load argus mesh and get disv grid properties
 
-fname = os.path.join(data_ws, "ex-gwf-disvmesh", "argus.exp")
+fname = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/ex-gwf-disvmesh/argus.exp",
+    known_hash="md5:072a758ca3d35831acb7e1e27e7b8524",
+)
 verts, iverts = from_argus_export(fname)
 gridprops = flopy.utils.cvfdutil.get_disv_gridprops(verts, iverts)
 cell_areas = []

scripts/ex-gwf-drn-p01.py

@@ -16,6 +16,7 @@
 import flopy
 import matplotlib.pyplot as plt
 import numpy as np
+import pooch
 from flopy.plot.styles import styles
 from modflow_devtools.latex import (build_table, exp_format, float_format,
                                     int_format)
@@ -30,7 +31,6 @@
 
 sim_name = "ex-gwf-drn-p01"
 ws = pl.Path("../examples")
-data_ws = pl.Path("../data")
 
 # Configuration
 
@@ -92,20 +92,36 @@
 
 # Load the idomain, top, bottom, and uzf/mvr arrays
 
-data_pth = data_ws / "ex-gwf-sfr-p01"
-fpth = os.path.join(data_pth, "idomain.txt")
+fpth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/ex-gwf-sfr-p01/idomain.txt",
+    known_hash="md5:a0b12472b8624aecdc79e5c19c97040c",
+)
 idomain = np.loadtxt(fpth, dtype=int)
-fpth = os.path.join(data_pth, "bottom.txt")
+fpth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/ex-gwf-sfr-p01/bottom.txt",
+    known_hash="md5:fa5fe276f4f58a01eabfe88516cc90af",
+)
 botm = np.loadtxt(fpth, dtype=float)
 
-data_pth = data_ws / sim_name
-fpth = os.path.join(data_pth, "top.txt")
+fpth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/top.txt",
+    known_hash="md5:88cc15f87824ebfd35ed5b4be7f68387",
+)
 top = np.loadtxt(fpth, dtype=float)
-fpth = os.path.join(data_pth, "infilt_mult.txt")
+fpth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/infilt_mult.txt",
+    known_hash="md5:8bf0a48d604263cb35151587a9d8ca29",
+)
 infilt_mult = np.loadtxt(fpth, dtype=float)
-fpth = os.path.join(data_pth, "extwc_mult.txt")
+fpth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/extwc_mult.txt",
+    known_hash="md5:6e289692a2b55b7bafb8bd9d71b0a2cb",
+)
 extwc_mult = np.loadtxt(fpth, dtype=float)
-fpth = os.path.join(data_pth, "routing_map.txt")
+fpth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/routing_map.txt",
+    known_hash="md5:1bf9a6bb3513a184aa5093485e622f5b",
+)
 routing_map = np.loadtxt(fpth, dtype=int)
 
 # convert routing map to zero-based reach numbers

scripts/ex-gwf-fhb.py

@@ -27,7 +27,6 @@
 
 sim_name = "ex-gwf-fhb"
 ws = pl.Path("../examples")
-data_ws = pl.Path("../data")
 
 # Configuration
 

scripts/ex-gwf-lak-p02.py

@@ -15,6 +15,7 @@
 import flopy
 import matplotlib.pyplot as plt
 import numpy as np
+import pooch
 import shapefile as shp
 from flopy.plot.styles import styles
 from modflow_devtools.misc import timed
@@ -130,12 +131,16 @@
 shape3d = (nlay, nrow, ncol)
 
 # Load the idomain arrays
-
-data_pth = os.path.join("..", "data", sim_name)
+fpth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/lakes-01.txt",
+    known_hash="md5:a74ded5357aa667b9df793847e5f8f41",
+)
 lake_map = np.ones(shape3d, dtype=int) * -1
-fpth = os.path.join(data_pth, "lakes-01.txt")
 lake_map[0, :, :] = np.loadtxt(fpth, dtype=int) - 1
-fpth = os.path.join(data_pth, "lakes-02.txt")
+fpth = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/lakes-02.txt",
+    known_hash="md5:7830e5223c958c35be349a3be24a60a3",
+)
 lake_map[1, :, :] = np.loadtxt(fpth, dtype=int) - 1
 
 # create linearly varying evapotranspiration surface

scripts/ex-gwf-lgrv.py

@@ -16,6 +16,7 @@
 import flopy.utils.lgrutil
 import matplotlib.pyplot as plt
 import numpy as np
+import pooch
 from flopy.plot.styles import styles
 from modflow_devtools.misc import timed
 
@@ -25,8 +26,8 @@
 
 # Base simulation and data workspace
 
+sim_name = "ex-gwf-lgrv"
 ws = pl.Path("../examples")
-data_ws = pl.Path("../data") / "ex-gwf-lgrv"
 
 # Configuration
 
@@ -42,9 +43,9 @@
 # Scenario parameters
 
 parameters = {
-    "ex-gwf-lgrv-gr": {"configuration": "Refined"},
-    "ex-gwf-lgrv-gc": {"configuration": "Coarse"},
-    "ex-gwf-lgrv-lgr": {"configuration": "LGR"},
+    f"{sim_name}-gr": {"configuration": "Refined"},
+    f"{sim_name}-gc": {"configuration": "Coarse"},
+    f"{sim_name}-lgr": {"configuration": "LGR"},
 }
 
 # Table LGRV Model Parameters
@@ -78,13 +79,49 @@
 
 # load data files and process into arrays
 
-fname = data_ws / "top.dat"
+fname = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/top.dat",
+    known_hash="md5:7e95923e78d0a2e2133929376d913ecf",
+)
 top = np.loadtxt(fname)
 ikzone = np.empty((nlay, nrow, ncol), dtype=float)
+hashes = [
+    "548876af515cb8db0c17e7cba0cda364",
+    "548876af515cb8db0c17e7cba0cda364",
+    "548876af515cb8db0c17e7cba0cda364",
+    "548876af515cb8db0c17e7cba0cda364",
+    "d91e2663ad671119de17a91ffb2a65ab",
+    "1702d97a83074669db86521b22b87304",
+    "da973a8edd77a44ed23a29661e2935eb",
+    "0d514a8f7b208e840a8e5cbfe4018c63",
+    "afd45ac7125f8351b73add34d35d8435",
+    "dc99e101996376e1dde1d172dea05f5d",
+    "fe8d2de0558245c9270597b01070403a",
+    "cd1fcf0fe2c807ff53eda80860bfe50f",
+    "c77b43c8045f5459a75547d575665134",
+    "2252b57927d7d01d0f9a0dda262397d7",
+    "1399c6872dd7c41be4b10c1251ad7c65",
+    "331e4006370cceb3864b881b7a0558d6",
+    "a46e66e632c5af7bf104900793509e5d",
+    "41f44eee3db48234069de4e9e329317b",
+    "8f4f1bdd6b6863c3ba68e2336f5ff70a",
+    "a1c856c05bcc4a17cf3fe61b87fbc720",
+    "a23fef9e866ad9763943150a3b70f5eb",
+    "02d67bc7f67814d2248c27cdd5205cf8",
+    "e7a07da76d111ef2229d26c553daa7eb",
+    "f6bc51735c4af81e1298d0efd06cd506",
+    "432b9a02f3ff4906a214b271c965ebad",
+]
 for k in range(nlay):
-    fname = data_ws / f"ikzone{k + 1}.dat"
+    fname = pooch.retrieve(
+        url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/ikzone{k + 1}.dat",
+        known_hash=f"md5:{hashes[k]}",
+    )
     ikzone[k, :, :] = np.loadtxt(fname)
-fname = data_ws / "riv.dat"
+fname = pooch.retrieve(
+    url=f"https://github.com/MODFLOW-USGS/modflow6-examples/raw/master/data/{sim_name}/riv.dat",
+    known_hash="md5:5ccbe4f29940376309db445dbb2d75d0",
+)
 dt = [
     ("k", int),
     ("i", int),